Feed protein supplements are one of the most expensive and limiting feed ingredients. This review offers a comprehensive analysis of how the expected expansion of animal production, driven by the rising world population and living standards for more animal-sourced foods, is creating a global shortage of feed protein supply. Because ruminants, chickens, and pigs contribute to 96% of the global supply of animal protein and aquaculture is growing fast, means of meeting the feed protein requirements of these species are elaborated. Geographic variation and interdependence among China, Europe, and North America in the demand and supply of feed protein are compared. The potential and current state of exploration into alternative feed proteins, including microalgae, insects, single-cell proteins, and coproducts, are highlighted. Strategic innovations are proposed to upgrade feed protein processing and assessment, improve protein digestion by exogenous enzymes, and genetically select feed-efficient livestock breeds. An overall successful and sustainable solution in meeting global feed protein demands will lead to a substantial net gain of human-edible animal protein with a minimal environmental footprint.

The data set used in the present study was obtained from 20 energy metabolism studies involving 579 lactating dairy cows (511 Holstein-Friesian, 36 Norwegian Red, and 32 Jersey-Holstein crossbreds) varying in genetic merit, lactation number, stage of lactation, and live weight. These cows were offered diets based on grass silage (n=550) or fresh grass (n=29), and their energy intake and outputs, including methane energy (CH(4)-E), were measured in indirect open-circuit respiration calorimeter chambers. The objective was to use these data to evaluate relationships between CH(4)-E output and a range of factors in animal production and energetic efficiency in lactating dairy cows under normal feeding regimens. The CH(4)-E as a proportion of milk energy output (E(l)), E(l) adjusted to zero energy balance (E(l(0))), or intakes of gross energy (GE), digestible energy (DE), or metabolizable energy (ME) was significantly related to a wide range of variables associated with milk production (E(l) and E(l(0))) and energy parameters (energy intake, metabolizability, partitioning, and utilization efficiencies). Three sets of linear relationships were developed with experimental effects removed. The CH(4)-E/GE intake (r(2)=0.50-0.62) and CH(4)-E/E(l) (r(2)=0.41-0.68) were reduced with increasing feeding level, E(l)/metabolic body weight (MBW; kg(0.75)), E(l(0))/MBW, GE intake/MBW, DE intake/MBW, and ME intake/MBW. Increasing dietary ME/DE decreased CH(4)-E/E(l) (r(2)=0.46) and CH(4)-E/GE intake (r(2)=0.72). Dietary ME concentration and ME/GE were also negatively related to CH(4)-E/GE intake (r(2)=0.47). However, increasing heat production/ME intake increased CH(4)-E as a proportion of E(l) (r(2)=0.41), E(l(0)) (r(2)=0.67) and energy intake (GE, DE, and ME; r(2)=0.62 and 0.70). These proportional CH(4)-E variables were reduced with increasing ratios of E(l)/ME intake and E(l(0))/ME intake and efficiency of ME use for lactation (r(2)=0.49-0.70). Fitting CH(4)-E/E(l) or CH(4)-E/E(l(0)) against these energetic efficiencies in quadratic rather than linear relationships significantly increased r(2) values (0.49-0.67 vs. 0.59-0.87). In conclusion, CH(4)-E as a proportion of energy intake (GE, DE, and ME) and milk production (E(l) and E(l(0))) can be reduced by increasing milk yield and energetic efficiency of milk production or by reducing energy expenditure for maintenance. The selection of dairy cows with high energy utilization efficiencies and milk productivity offers an effective approach to reducing enteric CH(4) emission rates.

Partners in Expert Working Group WG2 of the COST Action METHAGENE have used several methods for measuring methane output by individual dairy cattle under various environmental conditions. Methods included respiration chambers, the sulphur hexafluoride (SF₆) tracer technique, breath sampling during milking or feeding, the GreenFeed system, and the laser methane detector. The aim of the current study was to review and compare the suitability of methods for large-scale measurements of methane output by individual animals, which may be combined with other databases for genetic evaluations. Accuracy, precision and correlation between methods were assessed. Accuracy and precision are important, but data from different sources can be weighted or adjusted when combined if they are suitably correlated with the ‘true’ value. All methods showed high correlations with respiration chambers. Comparisons among alternative methods generally had lower correlations than comparisons with respiration chambers, despite higher numbers of animals and in most cases simultaneous repeated measures per cow per method. Lower correlations could be due to increased variability and imprecision of alternative methods, or maybe different aspects of methane emission are captured using different methods. Results confirm that there is sufficient correlation between methods for measurements from all methods to be combined for international genetic studies and provide a much-needed framework for comparing genetic correlations between methods should these become available.

A large data set derived from total diet digestibility assessments on lactating dairy cows (535 Holstein-Friesian and 29 Norwegian) was used to examine effects of dietary and animal factors on manure (feces and urine) nitrogen (N) output and to develop mitigation strategies and prediction equations for manure N output in lactating dairy cows. Manure N output was positively and significantly related to live weight, milk yield, dietary crude protein (CP) concentration, dry matter intake, and N intake. Reducing the dietary CP concentration or increasing the milk yield decreased manure N output per kilogram of milk yield. Prediction equations for manure N output using live weight and milk yield, either alone or combined, had relatively low R2 (0.227 to 0.474) and large standard error (70.6 to 85.6) values. Addition of dietary CP concentration to these relationships considerably increased R2 to 0.754 and reduced the standard error to 48.2. Relating manure N output to N intake produced a very high r2 (0.901) and a very low standard error (30.6). The addition of live weight and milk yield to this relationship as supporting predictors only marginally increased R2 to 0.910 and reduced the standard error to 29.3. The internal validation of these equations revealed that use of N intake as the primary predictor produced a very accurate prediction of manure N output. In situations in which data on N intake are not available, prediction equations based on dietary CP concentration, live weight, and milk yield together can produce a relatively accurate assessment of manure N output.

The objectives of the present study were to examine relationships between methane (CH4) output and animal and dietary factors, and to use these relationships to develop prediction equations for CH4 emission from beef cattle. The dataset was obtained from 108 growing-to-finishing beef steers in five studies and CH4 production and energy metabolism data were measured in indirect respiration calorimeter chambers. Dietary forage proportion ranged from 29.5% to 100% (dry matter (DM) basis) and forages included grass silage, fresh grass, dried grass and fodder beet. Linear and multiple regression techniques were used to examine relationships between CH4 emission and animal and dietary variables, with the effects of experiment or forage type removed. Total CH4 emission was positively related to live weight (LW), feeding level and intake of feed (DM and organic matter) and energy (gross energy (GE), digestible energy (DE) and metabolisable energy (ME)) (P < 0.001), while CH4/DM intake (DMI) was negatively related to energy digestibility and ME/GE (P < 0.05 or less). Using LW alone to predict CH4 emission produced a poor relationship when compared to DMI and GE intake (GEI) (R 2 = 0.26 v. 0.68 and 0.70 respectively). Adding feeding level, dietary NDF concentration and CP/ME or feeding level, energy digestibility and ME/GE to support LW resulted in a R 2 of 0.66 or 0.84. The high R 2 (0.84) was similar to that obtained using DMI or GEI together with energy digestibility and ME/GE as predictors. Further inclusion of dietary forage proportion and ADF and NDF concentration to the multiple relationships using GEI as the primary predictor resulted in a R 2 of 0.87. These equations were evaluated through internal validation, by developing a range of similar new equations from two-thirds of the present data and then validating these new equations with the remaining one-third of data. The validation indicated that addition of energy digestibility and ME/GE to support LW with feeding level, DMI and GEI considerably increased the prediction accuracy. It is concluded that CH4 emission of beef steers can be accurately predicted from LW plus feeding level, DMI or GEI together with energy digestibility and ME/GE. The dataset was also used to validate a range of prediction equations for CH4 production of cattle published elsewhere.

Data from 286 beef cattle, obtained in total diet digestibility assessments, were used to examine effects of dietary and animal factors on N excretion in feces and urine and to develop prediction equations for N excretion in beef cattle. The animals used were mainly from beef breeds, at various ages (from growth to finishing) and live BW (153 to 580 kg), and offered diets containing grass silage at production feeding levels. Dietary forage proportion ranged from 199 to 1,000 g/kg of DM and dietary CP concentration from 108 to 217 g/kg of DM. Linear and multiple regression techniques were used to examine relationships between the efficiency of N utilization and dietary and animal variables with the experimental effects removed. The statistical analysis indicated that N excretion was related positively (P < 0.001) to live BW and intakes of DM, N, and ME, and negatively (P < 0.001) to dietary forage proportion. The prediction equation for N excretion, developed using N intake alone, produced a large r² (0.898) and a small SE (12.3). Addition of live BW and forage proportion as supporting predictors to this relationship only marginally increased R² to 0.915 and reduced SE to 11.2. Nitrogen excretion was less well related to live BW (r² = 0.771, SE = 18.5) than to N intake. Addition of N intake as a proportion of DMI or ME intake to the relationship between live BW and N excretion increased R² to 0.824 and reduced SE to 16.2. The internal validation of these equations revealed that using N intake as the primary predictor produced a very accurate prediction of N excretion. In situations where data on N intake are not available, prediction equations based on live BW and dietary N concentration together can produce a relatively accurate assessment of N excretion. A number of mitigation strategies to reduce N excretion in feces and urine in beef cattle are discussed, including manipulation of dietary N concentration, diet quality, and level of feeding. The prediction equations and mitigation strategies developed in the current study provide an approach for beef producers to quantify N excretion against production and to develop their own mitigation strategies to reduce N excretion.